Advanced DWI has significant clinical potential, especially in certain neurosurgical procedures (e.g., DBS) where mapping of fine white matter tracts is crucial for optimizing surgical planning. Unfortunately, such advanced approaches often require a significant amount of scan time that is prohibitive for most clinical applications. We have focused on developing a novel imaging technique for DWI that is faster than the single-shot EPI (SS-EPI) sequence commonly used in diffusion imaging. Once developed, such a pulse sequence would allow higher angular resolution diffusion data to be acquired in a short period of time, thereby greatly increasing the clinical potential of advanced diffusion imaging techniques. The time dependence of the water diffusion function could reveal the physical properties on the cellular level, e.g., neuron/axon diameter, density and extra/intra-cellular water compartments. However, these experiments (e.g., ActiveAx, dPFG, and OGSE) have not been applied in clinical researches due to the long scan time. With the new fast sequence for diffusion, I would like to explore and implement these sequences for clinical scanners. My lab has started to work on computational neuroimaging using diffusion-imaging data to reveal fine structures of the brain and white matter connectivity. In particular, we are currently developing an auto-segmentation of amygdalae and white matter using machine learning, e.g., MVPA.